Overview

Dataset statistics

Number of variables28
Number of observations80
Missing cells31
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.6 KiB
Average record size in memory225.6 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-02" Constant
url has a high cardinality: 80 distinct values High cardinality
name has a high cardinality: 69 distinct values High cardinality
_embedded_show_url has a high cardinality: 66 distinct values High cardinality
_embedded_show_name has a high cardinality: 65 distinct values High cardinality
_embedded_show_premiered has a high cardinality: 60 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 59 distinct values High cardinality
_embedded_show_summary has a high cardinality: 53 distinct values High cardinality
_links_self_href has a high cardinality: 80 distinct values High cardinality
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
season is highly correlated with numberHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 10 other fieldsHigh correlation
summary is highly correlated with url and 3 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 4 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_summary and 4 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_status and 4 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
name is highly correlated with url and 2 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
airtime is highly correlated with url and 6 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_language is highly correlated with url and 3 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_type and 7 other fieldsHigh correlation
id is highly correlated with url and 10 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with id and 21 other fieldsHigh correlation
season is highly correlated with url and 14 other fieldsHigh correlation
number is highly correlated with url and 12 other fieldsHigh correlation
type is highly correlated with url and 10 other fieldsHigh correlation
airtime is highly correlated with url and 18 other fieldsHigh correlation
airstamp is highly correlated with url and 19 other fieldsHigh correlation
runtime is highly correlated with url and 18 other fieldsHigh correlation
summary is highly correlated with url and 11 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_type is highly correlated with url and 20 other fieldsHigh correlation
_embedded_show_language is highly correlated with url and 16 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_status is highly correlated with url and 12 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with url and 16 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with url and 15 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 10 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_weight is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 10 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
number has 1 (1.2%) missing values Missing
runtime has 6 (7.5%) missing values Missing
_embedded_show_runtime has 20 (25.0%) missing values Missing
_embedded_show_averageRuntime has 4 (5.0%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
_embedded_show_url is uniformly distributed Uniform
_embedded_show_name is uniformly distributed Uniform
_embedded_show_premiered is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 01:59:41.207174
Analysis finished2022-05-10 02:00:12.456794
Duration31.25 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021553.962
Minimum1945144
Maximum2318096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:12.521643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1945144
5-th percentile1965847.7
Q11976154.75
median1979292.5
Q32026184.75
95-th percentile2193289.2
Maximum2318096
Range372952
Interquartile range (IQR)50030

Descriptive statistics

Standard deviation81262.92541
Coefficient of variation (CV)0.04019824695
Kurtosis2.149141054
Mean2021553.962
Median Absolute Deviation (MAD)7118
Skewness1.762679926
Sum161724317
Variance6603663046
MonotonicityNot monotonic
2022-05-09T21:00:12.637608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19727821
 
1.2%
19792231
 
1.2%
19776331
 
1.2%
19776321
 
1.2%
19761861
 
1.2%
19761851
 
1.2%
19761551
 
1.2%
19761541
 
1.2%
19761371
 
1.2%
19761361
 
1.2%
Other values (70)70
87.5%
ValueCountFrequency (%)
19451441
1.2%
19588651
1.2%
19600291
1.2%
19644931
1.2%
19659191
1.2%
19685481
1.2%
19685491
1.2%
19701841
1.2%
19715671
1.2%
19727821
1.2%
ValueCountFrequency (%)
23180961
1.2%
22396071
1.2%
22059671
1.2%
22059661
1.2%
21926221
1.2%
21820801
1.2%
21761191
1.2%
21748981
1.2%
21661931
1.2%
21539151
1.2%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size768.0 B
https://www.tvmaze.com/episodes/1972782/kontakty-1x28-kontakty-v-telefone-sergea-lazareva-timati-polina-gagarina-vlad-topalov-ida-galic
 
1
https://www.tvmaze.com/episodes/1979223/kotiki-1x03-seria-3
 
1
https://www.tvmaze.com/episodes/1977633/to-love-1x22-episode-22
 
1
https://www.tvmaze.com/episodes/1977632/to-love-1x21-episode-21
 
1
https://www.tvmaze.com/episodes/1976186/psych-hunter-1x20-episode-20
 
1
Other values (75)
75 

Length

Max length140
Median length105
Mean length82.2875
Min length58

Characters and Unicode

Total characters6583
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1972782/kontakty-1x28-kontakty-v-telefone-sergea-lazareva-timati-polina-gagarina-vlad-topalov-ida-galic
2nd rowhttps://www.tvmaze.com/episodes/1979223/kotiki-1x03-seria-3
3rd rowhttps://www.tvmaze.com/episodes/1971567/mermaid-prince-2x07-episode-7
4th rowhttps://www.tvmaze.com/episodes/1983072/wan-sheng-jie-2x10-all-products-funds-were-spent-on-this-episode
5th rowhttps://www.tvmaze.com/episodes/1985615/yi-nian-yong-heng-1x19-episode-19

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1972782/kontakty-1x28-kontakty-v-telefone-sergea-lazareva-timati-polina-gagarina-vlad-topalov-ida-galic1
 
1.2%
https://www.tvmaze.com/episodes/1979223/kotiki-1x03-seria-31
 
1.2%
https://www.tvmaze.com/episodes/1977633/to-love-1x22-episode-221
 
1.2%
https://www.tvmaze.com/episodes/1977632/to-love-1x21-episode-211
 
1.2%
https://www.tvmaze.com/episodes/1976186/psych-hunter-1x20-episode-201
 
1.2%
https://www.tvmaze.com/episodes/1976185/psych-hunter-1x19-episode-191
 
1.2%
https://www.tvmaze.com/episodes/1976155/new-face-1x14-episode-141
 
1.2%
https://www.tvmaze.com/episodes/1976154/new-face-1x13-episode-131
 
1.2%
https://www.tvmaze.com/episodes/1976137/insect-detective-1x24-episode-241
 
1.2%
https://www.tvmaze.com/episodes/1976136/insect-detective-1x23-episode-231
 
1.2%
Other values (70)70
87.5%

Length

2022-05-09T21:00:12.763293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1972782/kontakty-1x28-kontakty-v-telefone-sergea-lazareva-timati-polina-gagarina-vlad-topalov-ida-galic1
 
1.2%
https://www.tvmaze.com/episodes/1979223/kotiki-1x03-seria-31
 
1.2%
https://www.tvmaze.com/episodes/1971567/mermaid-prince-2x07-episode-71
 
1.2%
https://www.tvmaze.com/episodes/1983072/wan-sheng-jie-2x10-all-products-funds-were-spent-on-this-episode1
 
1.2%
https://www.tvmaze.com/episodes/1985615/yi-nian-yong-heng-1x19-episode-191
 
1.2%
https://www.tvmaze.com/episodes/2030018/dolls-frontline-2x10-episode-101
 
1.2%
https://www.tvmaze.com/episodes/1973540/please-wait-brother-1x19-episode-191
 
1.2%
https://www.tvmaze.com/episodes/1973541/please-wait-brother-1x20-episode-201
 
1.2%
https://www.tvmaze.com/episodes/2066367/chu-feng-yi-dian-shizi-1x04-episode-41
 
1.2%
https://www.tvmaze.com/episodes/1982912/alex-og-aune-1x03-setter-bleik-pa-kartet1
 
1.2%
Other values (70)70
87.5%

Most occurring characters

ValueCountFrequency (%)
e565
 
8.6%
-506
 
7.7%
s425
 
6.5%
t408
 
6.2%
/400
 
6.1%
o364
 
5.5%
w278
 
4.2%
a274
 
4.2%
i263
 
4.0%
p252
 
3.8%
Other values (30)2848
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4513
68.6%
Decimal Number924
 
14.0%
Other Punctuation640
 
9.7%
Dash Punctuation506
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e565
12.5%
s425
 
9.4%
t408
 
9.0%
o364
 
8.1%
w278
 
6.2%
a274
 
6.1%
i263
 
5.8%
p252
 
5.6%
m229
 
5.1%
d193
 
4.3%
Other values (16)1262
28.0%
Decimal Number
ValueCountFrequency (%)
1222
24.0%
2124
13.4%
0115
12.4%
9106
11.5%
786
 
9.3%
857
 
6.2%
456
 
6.1%
655
 
6.0%
353
 
5.7%
550
 
5.4%
Other Punctuation
ValueCountFrequency (%)
/400
62.5%
.160
 
25.0%
:80
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4513
68.6%
Common2070
31.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e565
12.5%
s425
 
9.4%
t408
 
9.0%
o364
 
8.1%
w278
 
6.2%
a274
 
6.1%
i263
 
5.8%
p252
 
5.6%
m229
 
5.1%
d193
 
4.3%
Other values (16)1262
28.0%
Common
ValueCountFrequency (%)
-506
24.4%
/400
19.3%
1222
10.7%
.160
 
7.7%
2124
 
6.0%
0115
 
5.6%
9106
 
5.1%
786
 
4.2%
:80
 
3.9%
857
 
2.8%
Other values (4)214
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e565
 
8.6%
-506
 
7.7%
s425
 
6.5%
t408
 
6.2%
/400
 
6.1%
o364
 
5.5%
w278
 
4.2%
a274
 
4.2%
i263
 
4.0%
p252
 
3.8%
Other values (30)2848
43.3%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct69
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
Episode 19
 
4
Episode 10
 
3
Episode 2
 
2
Episode 7
 
2
Episode 20
 
2
Other values (64)
67 

Length

Max length86
Median length69
Mean length20.3375
Min length4

Characters and Unicode

Total characters1627
Distinct characters128
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)76.2%

Sample

1st rowКОНТАКТЫ в телефоне Сергея Лазарева: Тимати, Полина Гагарина, Влад Топалов, Ида Галич
2nd rowСерия 3
3rd rowEpisode 7
4th rowAll products funds were spent on this episode
5th rowEpisode 19

Common Values

ValueCountFrequency (%)
Episode 194
 
5.0%
Episode 103
 
3.8%
Episode 22
 
2.5%
Episode 72
 
2.5%
Episode 202
 
2.5%
Episode 92
 
2.5%
Episode 142
 
2.5%
Episode 132
 
2.5%
Episode 231
 
1.2%
Episode 11
 
1.2%
Other values (59)59
73.8%

Length

2022-05-09T21:00:12.872668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode34
 
12.5%
5
 
1.8%
194
 
1.5%
103
 
1.1%
23
 
1.1%
73
 
1.1%
the3
 
1.1%
93
 
1.1%
143
 
1.1%
a3
 
1.1%
Other values (198)207
76.4%

Most occurring characters

ValueCountFrequency (%)
191
 
11.7%
e126
 
7.7%
o89
 
5.5%
i77
 
4.7%
s67
 
4.1%
a63
 
3.9%
d58
 
3.6%
t56
 
3.4%
r55
 
3.4%
p51
 
3.1%
Other values (118)794
48.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1068
65.6%
Uppercase Letter250
 
15.4%
Space Separator191
 
11.7%
Decimal Number83
 
5.1%
Other Punctuation28
 
1.7%
Close Punctuation2
 
0.1%
Open Punctuation2
 
0.1%
Math Symbol2
 
0.1%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e126
 
11.8%
o89
 
8.3%
i77
 
7.2%
s67
 
6.3%
a63
 
5.9%
d58
 
5.4%
t56
 
5.2%
r55
 
5.1%
p51
 
4.8%
n42
 
3.9%
Other values (53)384
36.0%
Uppercase Letter
ValueCountFrequency (%)
E40
 
16.0%
S17
 
6.8%
T12
 
4.8%
C12
 
4.8%
A12
 
4.8%
Т11
 
4.4%
B10
 
4.0%
D8
 
3.2%
H7
 
2.8%
О7
 
2.8%
Other values (33)114
45.6%
Decimal Number
ValueCountFrequency (%)
122
26.5%
216
19.3%
010
12.0%
49
10.8%
78
 
9.6%
98
 
9.6%
35
 
6.0%
52
 
2.4%
62
 
2.4%
81
 
1.2%
Other Punctuation
ValueCountFrequency (%)
,14
50.0%
:4
 
14.3%
.3
 
10.7%
"2
 
7.1%
!2
 
7.1%
&2
 
7.1%
/1
 
3.6%
Space Separator
ValueCountFrequency (%)
191
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1035
63.6%
Common309
 
19.0%
Cyrillic283
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e126
 
12.2%
o89
 
8.6%
i77
 
7.4%
s67
 
6.5%
a63
 
6.1%
d58
 
5.6%
t56
 
5.4%
r55
 
5.3%
p51
 
4.9%
n42
 
4.1%
Other values (46)351
33.9%
Cyrillic
ValueCountFrequency (%)
а29
 
10.2%
о20
 
7.1%
и17
 
6.0%
е16
 
5.7%
л14
 
4.9%
н14
 
4.9%
р13
 
4.6%
Т11
 
3.9%
к11
 
3.9%
в9
 
3.2%
Other values (40)129
45.6%
Common
ValueCountFrequency (%)
191
61.8%
122
 
7.1%
216
 
5.2%
,14
 
4.5%
010
 
3.2%
49
 
2.9%
78
 
2.6%
98
 
2.6%
35
 
1.6%
:4
 
1.3%
Other values (12)22
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1337
82.2%
Cyrillic283
 
17.4%
None7
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
191
 
14.3%
e126
 
9.4%
o89
 
6.7%
i77
 
5.8%
s67
 
5.0%
a63
 
4.7%
d58
 
4.3%
t56
 
4.2%
r55
 
4.1%
p51
 
3.8%
Other values (62)504
37.7%
Cyrillic
ValueCountFrequency (%)
а29
 
10.2%
о20
 
7.1%
и17
 
6.0%
е16
 
5.7%
л14
 
4.9%
н14
 
4.9%
р13
 
4.6%
Т11
 
3.9%
к11
 
3.9%
в9
 
3.2%
Other values (40)129
45.6%
None
ValueCountFrequency (%)
ø2
28.6%
á1
14.3%
í1
14.3%
ó1
14.3%
å1
14.3%
æ1
14.3%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.1125
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:12.985199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33.25
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation534.6767625
Coefficient of variation (CV)3.46939257
Kurtosis9.042321396
Mean154.1125
Median Absolute Deviation (MAD)0
Skewness3.288743522
Sum12329
Variance285879.2403
MonotonicityNot monotonic
2022-05-09T21:00:13.059632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
148
60.0%
29
 
11.2%
20206
 
7.5%
44
 
5.0%
33
 
3.8%
73
 
3.8%
101
 
1.2%
111
 
1.2%
51
 
1.2%
181
 
1.2%
Other values (3)3
 
3.8%
ValueCountFrequency (%)
148
60.0%
29
 
11.2%
33
 
3.8%
44
 
5.0%
51
 
1.2%
73
 
3.8%
81
 
1.2%
101
 
1.2%
111
 
1.2%
141
 
1.2%
ValueCountFrequency (%)
20206
7.5%
311
 
1.2%
181
 
1.2%
141
 
1.2%
111
 
1.2%
101
 
1.2%
81
 
1.2%
73
3.8%
51
 
1.2%
44
5.0%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)53.2%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean30.18987342
Minimum1
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:13.184759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.9
Q17
median15
Q328
95-th percentile95.2
Maximum329
Range328
Interquartile range (IQR)21

Descriptive statistics

Standard deviation52.15240879
Coefficient of variation (CV)1.727480207
Kurtosis20.52011907
Mean30.18987342
Median Absolute Deviation (MAD)9
Skewness4.23973593
Sum2385
Variance2719.873742
MonotonicityNot monotonic
2022-05-09T21:00:13.294105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
195
 
6.2%
174
 
5.0%
104
 
5.0%
44
 
5.0%
94
 
5.0%
24
 
5.0%
14
 
5.0%
33
 
3.8%
73
 
3.8%
113
 
3.8%
Other values (32)41
51.2%
ValueCountFrequency (%)
14
5.0%
24
5.0%
33
3.8%
44
5.0%
51
 
1.2%
62
2.5%
73
3.8%
82
2.5%
94
5.0%
104
5.0%
ValueCountFrequency (%)
3291
1.2%
2881
1.2%
1431
1.2%
1061
1.2%
941
1.2%
821
1.2%
771
1.2%
601
1.2%
571
1.2%
552
2.5%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
regular
79 
significant_special
 
1

Length

Max length19
Median length7
Mean length7.15
Min length7

Characters and Unicode

Total characters572
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular79
98.8%
significant_special1
 
1.2%

Length

2022-05-09T21:00:13.388375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:00:13.502486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular79
98.8%
significant_special1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r158
27.6%
a81
14.2%
e80
14.0%
g80
14.0%
l80
14.0%
u79
13.8%
i4
 
0.7%
s2
 
0.3%
n2
 
0.3%
c2
 
0.3%
Other values (4)4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter571
99.8%
Connector Punctuation1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r158
27.7%
a81
14.2%
e80
14.0%
g80
14.0%
l80
14.0%
u79
13.8%
i4
 
0.7%
s2
 
0.4%
n2
 
0.4%
c2
 
0.4%
Other values (3)3
 
0.5%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin571
99.8%
Common1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r158
27.7%
a81
14.2%
e80
14.0%
g80
14.0%
l80
14.0%
u79
13.8%
i4
 
0.7%
s2
 
0.4%
n2
 
0.4%
c2
 
0.4%
Other values (3)3
 
0.5%
Common
ValueCountFrequency (%)
_1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r158
27.6%
a81
14.2%
e80
14.0%
g80
14.0%
l80
14.0%
u79
13.8%
i4
 
0.7%
s2
 
0.3%
n2
 
0.3%
c2
 
0.3%
Other values (4)4
 
0.7%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
2020-12-02
80 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-02
2nd row2020-12-02
3rd row2020-12-02
4th row2020-12-02
5th row2020-12-02

Common Values

ValueCountFrequency (%)
2020-12-0280
100.0%

Length

2022-05-09T21:00:13.573619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:00:13.667371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-0280
100.0%

Most occurring characters

ValueCountFrequency (%)
2320
40.0%
0240
30.0%
-160
20.0%
180
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number640
80.0%
Dash Punctuation160
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2320
50.0%
0240
37.5%
180
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2320
40.0%
0240
30.0%
-160
20.0%
180
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2320
40.0%
0240
30.0%
-160
20.0%
180
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
nan
42 
20:00
17 
12:00
 
4
10:00
 
3
06:00
 
2
Other values (10)
12 

Length

Max length5
Median length3
Mean length3.95
Min length3

Characters and Unicode

Total characters316
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)10.0%

Sample

1st row12:00
2nd rownan
3rd row11:00
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
nan42
52.5%
20:0017
21.2%
12:004
 
5.0%
10:003
 
3.8%
06:002
 
2.5%
21:002
 
2.5%
00:002
 
2.5%
11:001
 
1.2%
05:001
 
1.2%
17:351
 
1.2%
Other values (5)5
 
6.2%

Length

2022-05-09T21:00:13.745913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan42
52.5%
20:0017
21.2%
12:004
 
5.0%
10:003
 
3.8%
06:002
 
2.5%
21:002
 
2.5%
00:002
 
2.5%
11:001
 
1.2%
05:001
 
1.2%
17:351
 
1.2%
Other values (5)5
 
6.2%

Most occurring characters

ValueCountFrequency (%)
098
31.0%
n84
26.6%
a42
13.3%
:38
 
12.0%
225
 
7.9%
117
 
5.4%
54
 
1.3%
62
 
0.6%
32
 
0.6%
82
 
0.6%
Other values (2)2
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number152
48.1%
Lowercase Letter126
39.9%
Other Punctuation38
 
12.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
098
64.5%
225
 
16.4%
117
 
11.2%
54
 
2.6%
62
 
1.3%
32
 
1.3%
82
 
1.3%
71
 
0.7%
91
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
n84
66.7%
a42
33.3%
Other Punctuation
ValueCountFrequency (%)
:38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common190
60.1%
Latin126
39.9%

Most frequent character per script

Common
ValueCountFrequency (%)
098
51.6%
:38
 
20.0%
225
 
13.2%
117
 
8.9%
54
 
2.1%
62
 
1.1%
32
 
1.1%
82
 
1.1%
71
 
0.5%
91
 
0.5%
Latin
ValueCountFrequency (%)
n84
66.7%
a42
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
098
31.0%
n84
26.6%
a42
13.3%
:38
 
12.0%
225
 
7.9%
117
 
5.4%
54
 
1.3%
62
 
0.6%
32
 
0.6%
82
 
0.6%
Other values (2)2
 
0.6%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
2020-12-02T12:00:00+00:00
44 
2020-12-02T04:00:00+00:00
2020-12-02T11:00:00+00:00
 
4
2020-12-02T02:00:00+00:00
 
3
2020-12-02T17:00:00+00:00
 
3
Other values (16)
19 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2000
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)16.2%

Sample

1st row2020-12-02T00:00:00+00:00
2nd row2020-12-02T00:00:00+00:00
3rd row2020-12-02T02:00:00+00:00
4th row2020-12-02T02:00:00+00:00
5th row2020-12-02T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-02T12:00:00+00:0044
55.0%
2020-12-02T04:00:00+00:007
 
8.8%
2020-12-02T11:00:00+00:004
 
5.0%
2020-12-02T02:00:00+00:003
 
3.8%
2020-12-02T17:00:00+00:003
 
3.8%
2020-12-02T15:00:00+00:002
 
2.5%
2020-12-02T13:00:00+00:002
 
2.5%
2020-12-02T00:00:00+00:002
 
2.5%
2020-12-02T09:30:00+00:001
 
1.2%
2020-12-02T10:00:00+00:001
 
1.2%
Other values (11)11
 
13.8%

Length

2022-05-09T21:00:13.857184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-02t12:00:00+00:0044
55.0%
2020-12-02t04:00:00+00:007
 
8.8%
2020-12-02t11:00:00+00:004
 
5.0%
2020-12-02t02:00:00+00:003
 
3.8%
2020-12-02t17:00:00+00:003
 
3.8%
2020-12-02t15:00:00+00:002
 
2.5%
2020-12-02t13:00:00+00:002
 
2.5%
2020-12-02t00:00:00+00:002
 
2.5%
2020-12-02t05:35:00+00:001
 
1.2%
2020-12-03t02:25:00+00:001
 
1.2%
Other values (11)11
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0900
45.0%
2367
18.4%
:240
 
12.0%
-160
 
8.0%
1143
 
7.1%
T80
 
4.0%
+80
 
4.0%
48
 
0.4%
57
 
0.4%
37
 
0.4%
Other values (3)8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1440
72.0%
Other Punctuation240
 
12.0%
Dash Punctuation160
 
8.0%
Uppercase Letter80
 
4.0%
Math Symbol80
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0900
62.5%
2367
25.5%
1143
 
9.9%
48
 
0.6%
57
 
0.5%
37
 
0.5%
74
 
0.3%
93
 
0.2%
61
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:240
100.0%
Dash Punctuation
ValueCountFrequency (%)
-160
100.0%
Uppercase Letter
ValueCountFrequency (%)
T80
100.0%
Math Symbol
ValueCountFrequency (%)
+80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1920
96.0%
Latin80
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0900
46.9%
2367
19.1%
:240
 
12.5%
-160
 
8.3%
1143
 
7.4%
+80
 
4.2%
48
 
0.4%
57
 
0.4%
37
 
0.4%
74
 
0.2%
Other values (2)4
 
0.2%
Latin
ValueCountFrequency (%)
T80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0900
45.0%
2367
18.4%
:240
 
12.0%
-160
 
8.0%
1143
 
7.1%
T80
 
4.0%
+80
 
4.0%
48
 
0.4%
57
 
0.4%
37
 
0.4%
Other values (3)8
 
0.4%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)44.6%
Missing6
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean39.97297297
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:14.060116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q119
median37
Q345
95-th percentile120
Maximum300
Range298
Interquartile range (IQR)26

Descriptive statistics

Standard deviation40.85858437
Coefficient of variation (CV)1.022155255
Kurtosis22.40931821
Mean39.97297297
Median Absolute Deviation (MAD)11.5
Skewness3.987549571
Sum2958
Variance1669.423917
MonotonicityNot monotonic
2022-05-09T21:00:14.164148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4520
25.0%
57
 
8.8%
304
 
5.0%
253
 
3.8%
193
 
3.8%
1202
 
2.5%
352
 
2.5%
412
 
2.5%
132
 
2.5%
602
 
2.5%
Other values (23)27
33.8%
(Missing)6
 
7.5%
ValueCountFrequency (%)
21
 
1.2%
41
 
1.2%
57
8.8%
71
 
1.2%
91
 
1.2%
111
 
1.2%
121
 
1.2%
132
 
2.5%
141
 
1.2%
152
 
2.5%
ValueCountFrequency (%)
3001
 
1.2%
1281
 
1.2%
1211
 
1.2%
1202
 
2.5%
901
 
1.2%
691
 
1.2%
602
 
2.5%
481
 
1.2%
471
 
1.2%
4520
25.0%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
nan
67 
<p>Twin stars create an oxygen-rich atmosphere on Eden, where a teeming biosphere may parallel seasonal cycles of predation and reproduction on Earth.</p>
 
2
<p>Katya's ready for love, and she wants to do it now. Trixie explains to her what love is, and Katya designs her perfect man.</p>
 
1
<p>This issue could be a continuation of the series Better Houses about Castles of Ukraine. But Zhenya Sinelnikov decided to show us a slightly different route. Following from Kiev to Lviv, we will stop at a picturesque canyon, show a place with houses, right for a photo on Instagram, visit the famous tunnel of love. First of all, we will show the Ukrainian Disneyland - Victoria film studio, there is even a real Iron Throne! Next, we are waiting for the autumn Radomyshl, a visit to a shelter for bears, and at the end of the three castles of Ukraine - Pidhretsky Castle, Zolochy Castle, Olesky Castle.</p>
 
1
<p>Настало время расплаты и жестких вопросов для Владимира Маркони от ковбоев, амазонок и Сергея Мезенцева. Смотрим!</p>
 
1
Other values (8)

Length

Max length610
Median length3
Mean length38.775
Min length3

Characters and Unicode

Total characters3102
Distinct characters96
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)13.8%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan67
83.8%
<p>Twin stars create an oxygen-rich atmosphere on Eden, where a teeming biosphere may parallel seasonal cycles of predation and reproduction on Earth.</p>2
 
2.5%
<p>Katya's ready for love, and she wants to do it now. Trixie explains to her what love is, and Katya designs her perfect man.</p>1
 
1.2%
<p>This issue could be a continuation of the series Better Houses about Castles of Ukraine. But Zhenya Sinelnikov decided to show us a slightly different route. Following from Kiev to Lviv, we will stop at a picturesque canyon, show a place with houses, right for a photo on Instagram, visit the famous tunnel of love. First of all, we will show the Ukrainian Disneyland - Victoria film studio, there is even a real Iron Throne! Next, we are waiting for the autumn Radomyshl, a visit to a shelter for bears, and at the end of the three castles of Ukraine - Pidhretsky Castle, Zolochy Castle, Olesky Castle.</p>1
 
1.2%
<p>Настало время расплаты и жестких вопросов для Владимира Маркони от ковбоев, амазонок и Сергея Мезенцева. Смотрим!</p>1
 
1.2%
<p>During their occupation of large parts of Europe, the Nazis systematically looted foreign countries for art, gold and other items holding financial or cultural value. Often for any larger purpose, but for their own, egocentric, criminal gain.</p>1
 
1.2%
<p>Science and technology is marching on as the world enters the 1920's. But Americans have more to reckon with than just a new decade: every state in the country has gone "dry".</p>1
 
1.2%
<p>On exoplanet Atlas, dense gravity creates a thick atmosphere allowing airborne life forms to thrive — but also providing a lesson in adaptability.</p>1
 
1.2%
<p>Ants, scorpions and fireflies provide clues for biologists to conjecture about life on exoplanet Janus, including highly adaptable pentapods.</p>1
 
1.2%
<p>Welcome to the SEASON 2 of our spooky and now FESTIVE show- Too Many Spirits! Join us as we read your submitted holiday ghost stories and enjoy cocktails prepared by freshman bartender, Steven Lim.</p>1
 
1.2%
Other values (3)3
 
3.8%

Length

2022-05-09T21:00:14.274358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan67
 
12.5%
the21
 
3.9%
and15
 
2.8%
to15
 
2.8%
a14
 
2.6%
of13
 
2.4%
for9
 
1.7%
on7
 
1.3%
but5
 
0.9%
frankenstein4
 
0.7%
Other values (301)365
68.2%

Most occurring characters

ValueCountFrequency (%)
454
14.6%
n301
 
9.7%
a244
 
7.9%
e242
 
7.8%
o189
 
6.1%
t178
 
5.7%
i146
 
4.7%
s142
 
4.6%
r135
 
4.4%
l89
 
2.9%
Other values (86)982
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2386
76.9%
Space Separator455
 
14.7%
Uppercase Letter111
 
3.6%
Other Punctuation81
 
2.6%
Math Symbol52
 
1.7%
Dash Punctuation6
 
0.2%
Decimal Number5
 
0.2%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n301
12.6%
a244
 
10.2%
e242
 
10.1%
o189
 
7.9%
t178
 
7.5%
i146
 
6.1%
s142
 
6.0%
r135
 
5.7%
l89
 
3.7%
h88
 
3.7%
Other values (38)632
26.5%
Uppercase Letter
ValueCountFrequency (%)
R11
 
9.9%
T11
 
9.9%
F10
 
9.0%
S8
 
7.2%
E8
 
7.2%
C7
 
6.3%
B5
 
4.5%
H5
 
4.5%
A5
 
4.5%
O4
 
3.6%
Other values (16)37
33.3%
Other Punctuation
ValueCountFrequency (%)
,33
40.7%
.20
24.7%
/13
 
16.0%
!5
 
6.2%
'4
 
4.9%
"2
 
2.5%
;1
 
1.2%
:1
 
1.2%
&1
 
1.2%
?1
 
1.2%
Decimal Number
ValueCountFrequency (%)
22
40.0%
01
20.0%
91
20.0%
11
20.0%
Space Separator
ValueCountFrequency (%)
454
99.8%
 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
<26
50.0%
>26
50.0%
Dash Punctuation
ValueCountFrequency (%)
-5
83.3%
1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2402
77.4%
Common605
 
19.5%
Cyrillic95
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n301
12.5%
a244
 
10.2%
e242
 
10.1%
o189
 
7.9%
t178
 
7.4%
i146
 
6.1%
s142
 
5.9%
r135
 
5.6%
l89
 
3.7%
h88
 
3.7%
Other values (38)648
27.0%
Cyrillic
ValueCountFrequency (%)
о11
 
11.6%
а10
 
10.5%
е8
 
8.4%
р7
 
7.4%
и7
 
7.4%
в6
 
6.3%
т5
 
5.3%
м5
 
5.3%
л4
 
4.2%
с4
 
4.2%
Other values (16)28
29.5%
Common
ValueCountFrequency (%)
454
75.0%
,33
 
5.5%
<26
 
4.3%
>26
 
4.3%
.20
 
3.3%
/13
 
2.1%
!5
 
0.8%
-5
 
0.8%
'4
 
0.7%
(3
 
0.5%
Other values (12)16
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3005
96.9%
Cyrillic95
 
3.1%
Punctuation1
 
< 0.1%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
454
15.1%
n301
 
10.0%
a244
 
8.1%
e242
 
8.1%
o189
 
6.3%
t178
 
5.9%
i146
 
4.9%
s142
 
4.7%
r135
 
4.5%
l89
 
3.0%
Other values (58)885
29.5%
Cyrillic
ValueCountFrequency (%)
о11
 
11.6%
а10
 
10.5%
е8
 
8.4%
р7
 
7.4%
и7
 
7.4%
в6
 
6.3%
т5
 
5.3%
м5
 
5.3%
л4
 
4.2%
с4
 
4.2%
Other values (16)28
29.5%
Punctuation
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
 1
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47544.55
Minimum2266
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:14.384129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2266
5-th percentile26202.75
Q146057.25
median51870
Q352292.75
95-th percentile57989.8
Maximum61755
Range59489
Interquartile range (IQR)6235.5

Descriptive statistics

Standard deviation11313.18894
Coefficient of variation (CV)0.2379492274
Kurtosis5.694152009
Mean47544.55
Median Absolute Deviation (MAD)2745
Skewness-2.24664181
Sum3803564
Variance127988244
MonotonicityNot monotonic
2022-05-09T21:00:14.510967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
519274
 
5.0%
586892
 
2.5%
521062
 
2.5%
524212
 
2.5%
527802
 
2.5%
521592
 
2.5%
521082
 
2.5%
521072
 
2.5%
570302
 
2.5%
521042
 
2.5%
Other values (56)58
72.5%
ValueCountFrequency (%)
22661
1.2%
25041
1.2%
152501
1.2%
249631
1.2%
262681
1.2%
270551
1.2%
283461
1.2%
306061
1.2%
336911
1.2%
339441
1.2%
ValueCountFrequency (%)
617551
1.2%
595551
1.2%
586892
2.5%
579531
1.2%
576891
1.2%
574781
1.2%
570302
2.5%
567461
1.2%
565311
1.2%
560641
1.2%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct66
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
https://www.tvmaze.com/shows/51927/alien-worlds
 
4
https://www.tvmaze.com/shows/58689/my-supernatural-power
 
2
https://www.tvmaze.com/shows/52106/insect-detective
 
2
https://www.tvmaze.com/shows/52421/you-complete-me
 
2
https://www.tvmaze.com/shows/52780/mermaid-prince
 
2
Other values (61)
68 

Length

Max length73
Median length61
Mean length50.825
Min length41

Characters and Unicode

Total characters4066
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)67.5%

Sample

1st rowhttps://www.tvmaze.com/shows/49630/kontakty
2nd rowhttps://www.tvmaze.com/shows/52198/kotiki
3rd rowhttps://www.tvmaze.com/shows/47207/mermaid-prince
4th rowhttps://www.tvmaze.com/shows/48395/wan-sheng-jie
5th rowhttps://www.tvmaze.com/shows/49652/yi-nian-yong-heng

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/51927/alien-worlds4
 
5.0%
https://www.tvmaze.com/shows/58689/my-supernatural-power2
 
2.5%
https://www.tvmaze.com/shows/52106/insect-detective2
 
2.5%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
2.5%
https://www.tvmaze.com/shows/52780/mermaid-prince2
 
2.5%
https://www.tvmaze.com/shows/52159/to-love2
 
2.5%
https://www.tvmaze.com/shows/52108/psych-hunter2
 
2.5%
https://www.tvmaze.com/shows/52107/new-face2
 
2.5%
https://www.tvmaze.com/shows/57030/gjor-det-sjol2
 
2.5%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.5%
Other values (56)58
72.5%

Length

2022-05-09T21:00:14.624464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/51927/alien-worlds4
 
5.0%
https://www.tvmaze.com/shows/52107/new-face2
 
2.5%
https://www.tvmaze.com/shows/58689/my-supernatural-power2
 
2.5%
https://www.tvmaze.com/shows/51870/something-just-like-this2
 
2.5%
https://www.tvmaze.com/shows/52038/please-wait-brother2
 
2.5%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.5%
https://www.tvmaze.com/shows/57030/gjor-det-sjol2
 
2.5%
https://www.tvmaze.com/shows/52108/psych-hunter2
 
2.5%
https://www.tvmaze.com/shows/52159/to-love2
 
2.5%
https://www.tvmaze.com/shows/52780/mermaid-prince2
 
2.5%
Other values (56)58
72.5%

Most occurring characters

ValueCountFrequency (%)
/400
 
9.8%
w349
 
8.6%
t325
 
8.0%
s322
 
7.9%
o243
 
6.0%
e219
 
5.4%
h200
 
4.9%
m200
 
4.9%
.160
 
3.9%
a158
 
3.9%
Other values (29)1490
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2877
70.8%
Other Punctuation640
 
15.7%
Decimal Number403
 
9.9%
Dash Punctuation146
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w349
12.1%
t325
11.3%
s322
11.2%
o243
 
8.4%
e219
 
7.6%
h200
 
7.0%
m200
 
7.0%
a158
 
5.5%
c110
 
3.8%
p105
 
3.6%
Other values (15)646
22.5%
Decimal Number
ValueCountFrequency (%)
573
18.1%
448
11.9%
145
11.2%
243
10.7%
036
8.9%
735
8.7%
634
8.4%
932
7.9%
331
7.7%
826
 
6.5%
Other Punctuation
ValueCountFrequency (%)
/400
62.5%
.160
 
25.0%
:80
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2877
70.8%
Common1189
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
w349
12.1%
t325
11.3%
s322
11.2%
o243
 
8.4%
e219
 
7.6%
h200
 
7.0%
m200
 
7.0%
a158
 
5.5%
c110
 
3.8%
p105
 
3.6%
Other values (15)646
22.5%
Common
ValueCountFrequency (%)
/400
33.6%
.160
 
13.5%
-146
 
12.3%
:80
 
6.7%
573
 
6.1%
448
 
4.0%
145
 
3.8%
243
 
3.6%
036
 
3.0%
735
 
2.9%
Other values (4)123
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/400
 
9.8%
w349
 
8.6%
t325
 
8.0%
s322
 
7.9%
o243
 
6.0%
e219
 
5.4%
h200
 
4.9%
m200
 
4.9%
.160
 
3.9%
a158
 
3.9%
Other values (29)1490
36.6%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
Alien Worlds
 
4
Mermaid Prince
 
3
My Supernatural Power
 
2
Twisted Fate of Love
 
2
You Complete Me
 
2
Other values (60)
67 

Length

Max length40
Median length27
Mean length16.1125
Min length6

Characters and Unicode

Total characters1289
Distinct characters93
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)66.2%

Sample

1st rowКонтакты
2nd rowКотики
3rd rowMermaid Prince
4th rowWan Sheng Jie
5th rowYi Nian Yong Heng

Common Values

ValueCountFrequency (%)
Alien Worlds4
 
5.0%
Mermaid Prince3
 
3.8%
My Supernatural Power2
 
2.5%
Twisted Fate of Love2
 
2.5%
You Complete Me2
 
2.5%
To Love2
 
2.5%
Psych Hunter2
 
2.5%
New Face2
 
2.5%
Insect Detective2
 
2.5%
Gjør det sjøl2
 
2.5%
Other values (55)57
71.2%

Length

2022-05-09T21:00:14.741709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the6
 
2.6%
alien4
 
1.8%
love4
 
1.8%
worlds4
 
1.8%
new3
 
1.3%
of3
 
1.3%
yi3
 
1.3%
you3
 
1.3%
prince3
 
1.3%
mermaid3
 
1.3%
Other values (164)191
84.1%

Most occurring characters

ValueCountFrequency (%)
147
 
11.4%
e126
 
9.8%
i64
 
5.0%
o60
 
4.7%
a60
 
4.7%
n59
 
4.6%
r57
 
4.4%
s54
 
4.2%
t53
 
4.1%
l45
 
3.5%
Other values (83)564
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter913
70.8%
Uppercase Letter206
 
16.0%
Space Separator147
 
11.4%
Other Punctuation18
 
1.4%
Decimal Number5
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e126
13.8%
i64
 
7.0%
o60
 
6.6%
a60
 
6.6%
n59
 
6.5%
r57
 
6.2%
s54
 
5.9%
t53
 
5.8%
l45
 
4.9%
h35
 
3.8%
Other values (42)300
32.9%
Uppercase Letter
ValueCountFrequency (%)
T23
 
11.2%
S20
 
9.7%
W16
 
7.8%
M15
 
7.3%
P11
 
5.3%
C11
 
5.3%
A10
 
4.9%
D8
 
3.9%
Y8
 
3.9%
N8
 
3.9%
Other values (22)76
36.9%
Other Punctuation
ValueCountFrequency (%)
'5
27.8%
:5
27.8%
.4
22.2%
,3
16.7%
&1
 
5.6%
Decimal Number
ValueCountFrequency (%)
02
40.0%
22
40.0%
11
20.0%
Space Separator
ValueCountFrequency (%)
147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1005
78.0%
Common170
 
13.2%
Cyrillic114
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e126
 
12.5%
i64
 
6.4%
o60
 
6.0%
a60
 
6.0%
n59
 
5.9%
r57
 
5.7%
s54
 
5.4%
t53
 
5.3%
l45
 
4.5%
h35
 
3.5%
Other values (40)392
39.0%
Cyrillic
ValueCountFrequency (%)
о13
 
11.4%
р10
 
8.8%
к9
 
7.9%
т9
 
7.9%
е8
 
7.0%
и7
 
6.1%
а7
 
6.1%
м5
 
4.4%
с5
 
4.4%
н5
 
4.4%
Other values (24)36
31.6%
Common
ValueCountFrequency (%)
147
86.5%
'5
 
2.9%
:5
 
2.9%
.4
 
2.4%
,3
 
1.8%
02
 
1.2%
22
 
1.2%
11
 
0.6%
&1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1168
90.6%
Cyrillic114
 
8.8%
None7
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147
 
12.6%
e126
 
10.8%
i64
 
5.5%
o60
 
5.1%
a60
 
5.1%
n59
 
5.1%
r57
 
4.9%
s54
 
4.6%
t53
 
4.5%
l45
 
3.9%
Other values (48)443
37.9%
Cyrillic
ValueCountFrequency (%)
о13
 
11.4%
р10
 
8.8%
к9
 
7.9%
т9
 
7.9%
е8
 
7.0%
и7
 
6.1%
а7
 
6.1%
м5
 
4.4%
с5
 
4.4%
н5
 
4.4%
Other values (24)36
31.6%
None
ValueCountFrequency (%)
ø7
100.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
Scripted
33 
Talk Show
12 
Animation
Reality
Documentary
Other values (5)

Length

Max length11
Median length10
Mean length8.3875
Min length4

Characters and Unicode

Total characters671
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.5%

Sample

1st rowGame Show
2nd rowScripted
3rd rowScripted
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted33
41.2%
Talk Show12
 
15.0%
Animation9
 
11.2%
Reality9
 
11.2%
Documentary8
 
10.0%
Game Show3
 
3.8%
Variety2
 
2.5%
Sports2
 
2.5%
Award Show1
 
1.2%
News1
 
1.2%

Length

2022-05-09T21:00:14.874708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:00:15.011091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted33
34.4%
show16
16.7%
talk12
 
12.5%
animation9
 
9.4%
reality9
 
9.4%
documentary8
 
8.3%
game3
 
3.1%
variety2
 
2.1%
sports2
 
2.1%
award1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t63
 
9.4%
i62
 
9.2%
e56
 
8.3%
S51
 
7.6%
r46
 
6.9%
a44
 
6.6%
c41
 
6.1%
p35
 
5.2%
o35
 
5.2%
d34
 
5.1%
Other values (17)204
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter559
83.3%
Uppercase Letter96
 
14.3%
Space Separator16
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t63
11.3%
i62
11.1%
e56
10.0%
r46
 
8.2%
a44
 
7.9%
c41
 
7.3%
p35
 
6.3%
o35
 
6.3%
d34
 
6.1%
n26
 
4.7%
Other values (8)117
20.9%
Uppercase Letter
ValueCountFrequency (%)
S51
53.1%
T12
 
12.5%
A10
 
10.4%
R9
 
9.4%
D8
 
8.3%
G3
 
3.1%
V2
 
2.1%
N1
 
1.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin655
97.6%
Common16
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t63
 
9.6%
i62
 
9.5%
e56
 
8.5%
S51
 
7.8%
r46
 
7.0%
a44
 
6.7%
c41
 
6.3%
p35
 
5.3%
o35
 
5.3%
d34
 
5.2%
Other values (16)188
28.7%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t63
 
9.4%
i62
 
9.2%
e56
 
8.3%
S51
 
7.6%
r46
 
6.9%
a44
 
6.6%
c41
 
6.1%
p35
 
5.2%
o35
 
5.2%
d34
 
5.1%
Other values (17)204
30.4%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
Chinese
25 
English
22 
Russian
Norwegian
Arabic
Other values (12)
15 

Length

Max length10
Median length7
Mean length7.075
Min length3

Characters and Unicode

Total characters566
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)12.5%

Sample

1st rowRussian
2nd rowRussian
3rd rowKorean
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Chinese25
31.2%
English22
27.5%
Russian8
 
10.0%
Norwegian7
 
8.8%
Arabic3
 
3.8%
Korean3
 
3.8%
Japanese2
 
2.5%
Ukrainian1
 
1.2%
Portuguese1
 
1.2%
nan1
 
1.2%
Other values (7)7
 
8.8%

Length

2022-05-09T21:00:15.188597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese25
31.2%
english22
27.5%
russian8
 
10.0%
norwegian7
 
8.8%
arabic3
 
3.8%
korean3
 
3.8%
japanese2
 
2.5%
spanish1
 
1.2%
kazakh1
 
1.2%
swedish1
 
1.2%
Other values (7)7
 
8.8%

Most occurring characters

ValueCountFrequency (%)
n75
13.3%
i71
12.5%
e68
12.0%
s68
12.0%
h53
9.4%
a34
 
6.0%
g31
 
5.5%
C25
 
4.4%
E22
 
3.9%
l22
 
3.9%
Other values (23)97
17.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter487
86.0%
Uppercase Letter79
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n75
15.4%
i71
14.6%
e68
14.0%
s68
14.0%
h53
10.9%
a34
7.0%
g31
6.4%
l22
 
4.5%
r17
 
3.5%
u12
 
2.5%
Other values (9)36
7.4%
Uppercase Letter
ValueCountFrequency (%)
C25
31.6%
E22
27.8%
R8
 
10.1%
N7
 
8.9%
K4
 
5.1%
A3
 
3.8%
J2
 
2.5%
S2
 
2.5%
U1
 
1.3%
P1
 
1.3%
Other values (4)4
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin566
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n75
13.3%
i71
12.5%
e68
12.0%
s68
12.0%
h53
9.4%
a34
 
6.0%
g31
 
5.5%
C25
 
4.4%
E22
 
3.9%
l22
 
3.9%
Other values (23)97
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n75
13.3%
i71
12.5%
e68
12.0%
s68
12.0%
h53
9.4%
a34
 
6.0%
g31
 
5.5%
C25
 
4.4%
E22
 
3.9%
l22
 
3.9%
Other values (23)97
17.1%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
[]
21 
['Comedy']
['Drama', 'Romance']
['Drama', 'Thriller', 'Mystery']
['Science-Fiction', 'Nature']
Other values (26)
36 

Length

Max length40
Median length34.5
Mean length17
Min length2

Characters and Unicode

Total characters1360
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)21.2%

Sample

1st row[]
2nd row['Comedy']
3rd row['Drama', 'Romance', 'Mystery']
4th row['Comedy', 'Anime', 'Supernatural']
5th row['Comedy', 'Action', 'Anime', 'Fantasy']

Common Values

ValueCountFrequency (%)
[]21
26.2%
['Comedy']8
 
10.0%
['Drama', 'Romance']7
 
8.8%
['Drama', 'Thriller', 'Mystery']4
 
5.0%
['Science-Fiction', 'Nature']4
 
5.0%
['Music']3
 
3.8%
['Drama', 'Comedy', 'Romance']2
 
2.5%
['Comedy', 'Fantasy', 'Romance']2
 
2.5%
['Crime', 'Thriller', 'Mystery']2
 
2.5%
['Action', 'Crime', 'Thriller']2
 
2.5%
Other values (21)25
31.2%

Length

2022-05-09T21:00:15.317069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21
14.5%
comedy21
14.5%
drama21
14.5%
romance17
11.7%
thriller8
 
5.5%
mystery7
 
4.8%
children6
 
4.1%
anime5
 
3.4%
action5
 
3.4%
science-fiction5
 
3.4%
Other values (12)29
20.0%

Most occurring characters

ValueCountFrequency (%)
'248
18.2%
e87
 
6.4%
[80
 
5.9%
]80
 
5.9%
a80
 
5.9%
r71
 
5.2%
m70
 
5.1%
,65
 
4.8%
65
 
4.8%
o53
 
3.9%
Other values (26)461
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter686
50.4%
Other Punctuation313
23.0%
Uppercase Letter131
 
9.6%
Open Punctuation80
 
5.9%
Close Punctuation80
 
5.9%
Space Separator65
 
4.8%
Dash Punctuation5
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e87
12.7%
a80
11.7%
r71
10.3%
m70
10.2%
o53
7.7%
i52
7.6%
n50
7.3%
y45
6.6%
c40
 
5.8%
t33
 
4.8%
Other values (7)105
15.3%
Uppercase Letter
ValueCountFrequency (%)
C31
23.7%
D22
16.8%
R17
13.0%
F11
 
8.4%
A11
 
8.4%
M10
 
7.6%
T9
 
6.9%
S8
 
6.1%
N4
 
3.1%
H4
 
3.1%
Other values (3)4
 
3.1%
Other Punctuation
ValueCountFrequency (%)
'248
79.2%
,65
 
20.8%
Open Punctuation
ValueCountFrequency (%)
[80
100.0%
Close Punctuation
ValueCountFrequency (%)
]80
100.0%
Space Separator
ValueCountFrequency (%)
65
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin817
60.1%
Common543
39.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e87
 
10.6%
a80
 
9.8%
r71
 
8.7%
m70
 
8.6%
o53
 
6.5%
i52
 
6.4%
n50
 
6.1%
y45
 
5.5%
c40
 
4.9%
t33
 
4.0%
Other values (20)236
28.9%
Common
ValueCountFrequency (%)
'248
45.7%
[80
 
14.7%
]80
 
14.7%
,65
 
12.0%
65
 
12.0%
-5
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'248
18.2%
e87
 
6.4%
[80
 
5.9%
]80
 
5.9%
a80
 
5.9%
r71
 
5.2%
m70
 
5.1%
,65
 
4.8%
65
 
4.8%
o53
 
3.9%
Other values (26)461
33.9%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
Running
46 
Ended
31 
To Be Determined
 
3

Length

Max length16
Median length7
Mean length6.5625
Min length5

Characters and Unicode

Total characters525
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowEnded
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running46
57.5%
Ended31
38.8%
To Be Determined3
 
3.8%

Length

2022-05-09T21:00:15.427544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:00:15.558349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running46
53.5%
ended31
36.0%
to3
 
3.5%
be3
 
3.5%
determined3
 
3.5%

Most occurring characters

ValueCountFrequency (%)
n172
32.8%
d65
 
12.4%
i49
 
9.3%
R46
 
8.8%
u46
 
8.8%
g46
 
8.8%
e43
 
8.2%
E31
 
5.9%
6
 
1.1%
T3
 
0.6%
Other values (6)18
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter433
82.5%
Uppercase Letter86
 
16.4%
Space Separator6
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n172
39.7%
d65
 
15.0%
i49
 
11.3%
u46
 
10.6%
g46
 
10.6%
e43
 
9.9%
o3
 
0.7%
t3
 
0.7%
r3
 
0.7%
m3
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
R46
53.5%
E31
36.0%
T3
 
3.5%
B3
 
3.5%
D3
 
3.5%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin519
98.9%
Common6
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n172
33.1%
d65
 
12.5%
i49
 
9.4%
R46
 
8.9%
u46
 
8.9%
g46
 
8.9%
e43
 
8.3%
E31
 
6.0%
T3
 
0.6%
o3
 
0.6%
Other values (5)15
 
2.9%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n172
32.8%
d65
 
12.4%
i49
 
9.3%
R46
 
8.8%
u46
 
8.8%
g46
 
8.8%
e43
 
8.2%
E31
 
5.9%
6
 
1.1%
T3
 
0.6%
Other values (6)18
 
3.4%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22
Distinct (%)36.7%
Missing20
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean43.65
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:15.643256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q122.25
median40
Q345
95-th percentile120
Maximum300
Range298
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation42.50496481
Coefficient of variation (CV)0.9737678079
Kurtosis22.4174587
Mean43.65
Median Absolute Deviation (MAD)10
Skewness4.070760639
Sum2619
Variance1806.672034
MonotonicityNot monotonic
2022-05-09T21:00:15.737462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4519
23.8%
305
 
6.2%
205
 
6.2%
53
 
3.8%
253
 
3.8%
1203
 
3.8%
603
 
3.8%
372
 
2.5%
122
 
2.5%
402
 
2.5%
Other values (12)13
16.2%
(Missing)20
25.0%
ValueCountFrequency (%)
21
 
1.2%
41
 
1.2%
53
3.8%
81
 
1.2%
122
 
2.5%
151
 
1.2%
191
 
1.2%
205
6.2%
231
 
1.2%
253
3.8%
ValueCountFrequency (%)
3001
 
1.2%
1203
 
3.8%
902
 
2.5%
603
 
3.8%
551
 
1.2%
4519
23.8%
402
 
2.5%
372
 
2.5%
351
 
1.2%
331
 
1.2%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)48.7%
Missing4
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean38.36842105
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:15.847338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q117.25
median37.5
Q345
95-th percentile91.75
Maximum300
Range298
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation38.43361102
Coefficient of variation (CV)1.001699053
Kurtosis28.62001043
Mean38.36842105
Median Absolute Deviation (MAD)12.5
Skewness4.501260689
Sum2916
Variance1477.142456
MonotonicityNot monotonic
2022-05-09T21:00:15.966093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4518
22.5%
55
 
6.2%
254
 
5.0%
444
 
5.0%
373
 
3.8%
303
 
3.8%
203
 
3.8%
402
 
2.5%
602
 
2.5%
122
 
2.5%
Other values (27)30
37.5%
(Missing)4
 
5.0%
ValueCountFrequency (%)
21
 
1.2%
41
 
1.2%
55
6.2%
61
 
1.2%
71
 
1.2%
81
 
1.2%
91
 
1.2%
102
 
2.5%
122
 
2.5%
131
 
1.2%
ValueCountFrequency (%)
3001
 
1.2%
1201
 
1.2%
1101
 
1.2%
971
 
1.2%
901
 
1.2%
751
 
1.2%
602
 
2.5%
591
 
1.2%
541
 
1.2%
4518
22.5%

_embedded_show_premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct60
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size768.0 B
2020-12-02
 
6
2020-11-23
 
4
2020-11-18
 
3
2020-11-03
 
2
2020-11-25
 
2
Other values (55)
63 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)58.8%

Sample

1st row2019-04-03
2nd row2020-11-30
3rd row2020-04-14
4th row2020-04-01
5th row2020-08-12

Common Values

ValueCountFrequency (%)
2020-12-026
 
7.5%
2020-11-234
 
5.0%
2020-11-183
 
3.8%
2020-11-032
 
2.5%
2020-11-252
 
2.5%
2020-11-192
 
2.5%
2020-11-042
 
2.5%
2020-11-242
 
2.5%
2020-07-082
 
2.5%
2020-10-072
 
2.5%
Other values (50)53
66.2%

Length

2022-05-09T21:00:16.074383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-026
 
7.5%
2020-11-234
 
5.0%
2020-11-183
 
3.8%
2020-07-082
 
2.5%
2020-11-302
 
2.5%
2020-11-172
 
2.5%
2020-10-072
 
2.5%
2020-11-082
 
2.5%
2020-11-242
 
2.5%
2020-11-042
 
2.5%
Other values (50)53
66.2%

Most occurring characters

ValueCountFrequency (%)
0211
26.4%
2172
21.5%
-160
20.0%
1138
17.2%
928
 
3.5%
422
 
2.8%
821
 
2.6%
320
 
2.5%
511
 
1.4%
711
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number640
80.0%
Dash Punctuation160
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0211
33.0%
2172
26.9%
1138
21.6%
928
 
4.4%
422
 
3.4%
821
 
3.3%
320
 
3.1%
511
 
1.7%
711
 
1.7%
66
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0211
26.4%
2172
21.5%
-160
20.0%
1138
17.2%
928
 
3.5%
422
 
2.8%
821
 
2.6%
320
 
2.5%
511
 
1.4%
711
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0211
26.4%
2172
21.5%
-160
20.0%
1138
17.2%
928
 
3.5%
422
 
2.8%
821
 
2.6%
320
 
2.5%
511
 
1.4%
711
 
1.4%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
nan
49 
2020-12-02
2020-12-30
2020-12-16
 
3
2020-12-08
 
2
Other values (10)
13 

Length

Max length10
Median length3
Mean length5.7125
Min length3

Characters and Unicode

Total characters457
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.8%

Sample

1st rownan
2nd row2020-12-11
3rd row2020-12-10
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan49
61.3%
2020-12-028
 
10.0%
2020-12-305
 
6.2%
2020-12-163
 
3.8%
2020-12-082
 
2.5%
2020-12-032
 
2.5%
2020-12-232
 
2.5%
2021-01-142
 
2.5%
2020-12-111
 
1.2%
2020-12-101
 
1.2%
Other values (5)5
 
6.2%

Length

2022-05-09T21:00:16.290032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan49
61.3%
2020-12-028
 
10.0%
2020-12-305
 
6.2%
2020-12-163
 
3.8%
2020-12-082
 
2.5%
2020-12-032
 
2.5%
2020-12-232
 
2.5%
2021-01-142
 
2.5%
2020-12-111
 
1.2%
2020-12-101
 
1.2%
Other values (5)5
 
6.2%

Most occurring characters

ValueCountFrequency (%)
2102
22.3%
n98
21.4%
081
17.7%
-62
13.6%
a49
10.7%
144
9.6%
39
 
2.0%
63
 
0.7%
83
 
0.7%
43
 
0.7%
Other values (3)3
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number248
54.3%
Lowercase Letter147
32.2%
Dash Punctuation62
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2102
41.1%
081
32.7%
144
17.7%
39
 
3.6%
63
 
1.2%
83
 
1.2%
43
 
1.2%
71
 
0.4%
51
 
0.4%
91
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n98
66.7%
a49
33.3%
Dash Punctuation
ValueCountFrequency (%)
-62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common310
67.8%
Latin147
32.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2102
32.9%
081
26.1%
-62
20.0%
144
14.2%
39
 
2.9%
63
 
1.0%
83
 
1.0%
43
 
1.0%
71
 
0.3%
51
 
0.3%
Latin
ValueCountFrequency (%)
n98
66.7%
a49
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2102
22.3%
n98
21.4%
081
17.7%
-62
13.6%
a49
10.7%
144
9.6%
39
 
2.0%
63
 
0.7%
83
 
0.7%
43
 
0.7%
Other values (3)3
 
0.7%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Memory size768.0 B
nan
13 
https://www.netflix.com/title/80221410
 
4
https://tv.nrk.no/serie/gjoer-det-sjoel
 
2
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=
 
2
https://v.youku.com/v_show/id_XNDg2OTQ0ODAwOA==.html?s=dfbc7998206c499cac28
 
2
Other values (54)
57 

Length

Max length97
Median length72
Mean length43.65
Min length3

Characters and Unicode

Total characters3492
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)63.7%

Sample

1st rowhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI
2nd rowhttp://epic-media.ru/project/kotiki
3rd rownan
4th rowhttps://v.qq.com/detail/a/awnia0n2erqryf3.html
5th rowhttps://v.qq.com/detail/w/ww18u675tfmhas6.html

Common Values

ValueCountFrequency (%)
nan13
 
16.2%
https://www.netflix.com/title/802214104
 
5.0%
https://tv.nrk.no/serie/gjoer-det-sjoel2
 
2.5%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=2
 
2.5%
https://v.youku.com/v_show/id_XNDg2OTQ0ODAwOA==.html?s=dfbc7998206c499cac282
 
2.5%
https://v.qq.com/detail/m/mzc00200tu76tos.html2
 
2.5%
https://www.iqiyi.com/a_19rrhskr95.html2
 
2.5%
https://so.youku.com/search_video/q_%20%E6%9C%80%E5%88%9D%E7%9A%84%E7%9B%B8%E9%81%87?searchfrom=12
 
2.5%
https://www.youtube.com/c/HelloKittyFriends1
 
1.2%
https://elcinema.com/work/2061816/1
 
1.2%
Other values (49)49
61.3%

Length

2022-05-09T21:00:16.400162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan13
 
16.2%
https://www.netflix.com/title/802214104
 
5.0%
https://tv.nrk.no/serie/gjoer-det-sjoel2
 
2.5%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.5%
https://v.youku.com/v_show/id_xndg2otq0odawoa==.html?s=dfbc7998206c499cac282
 
2.5%
https://v.qq.com/detail/m/mzc00200tu76tos.html2
 
2.5%
https://www.iqiyi.com/a_19rrhskr95.html2
 
2.5%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
2.5%
http://tv3.ru/project/slepaya1
 
1.2%
http://www.njpw1972.com1
 
1.2%
Other values (49)49
61.3%

Most occurring characters

ValueCountFrequency (%)
/274
 
7.8%
t266
 
7.6%
s182
 
5.2%
e159
 
4.6%
o158
 
4.5%
w143
 
4.1%
.133
 
3.8%
h127
 
3.6%
a112
 
3.2%
i109
 
3.1%
Other values (64)1829
52.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2280
65.3%
Other Punctuation556
 
15.9%
Decimal Number297
 
8.5%
Uppercase Letter266
 
7.6%
Dash Punctuation46
 
1.3%
Math Symbol29
 
0.8%
Connector Punctuation18
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t266
 
11.7%
s182
 
8.0%
e159
 
7.0%
o158
 
6.9%
w143
 
6.3%
h127
 
5.6%
a112
 
4.9%
i109
 
4.8%
p105
 
4.6%
n104
 
4.6%
Other values (16)815
35.7%
Uppercase Letter
ValueCountFrequency (%)
E26
 
9.8%
A22
 
8.3%
B19
 
7.1%
P15
 
5.6%
Y14
 
5.3%
L12
 
4.5%
H11
 
4.1%
C11
 
4.1%
D11
 
4.1%
Q11
 
4.1%
Other values (16)114
42.9%
Decimal Number
ValueCountFrequency (%)
044
14.8%
938
12.8%
137
12.5%
835
11.8%
233
11.1%
528
9.4%
425
8.4%
624
8.1%
720
6.7%
313
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/274
49.3%
.133
23.9%
:67
 
12.1%
%57
 
10.3%
?16
 
2.9%
&7
 
1.3%
#1
 
0.2%
!1
 
0.2%
Math Symbol
ValueCountFrequency (%)
=27
93.1%
+2
 
6.9%
Dash Punctuation
ValueCountFrequency (%)
-46
100.0%
Connector Punctuation
ValueCountFrequency (%)
_18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2546
72.9%
Common946
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t266
 
10.4%
s182
 
7.1%
e159
 
6.2%
o158
 
6.2%
w143
 
5.6%
h127
 
5.0%
a112
 
4.4%
i109
 
4.3%
p105
 
4.1%
n104
 
4.1%
Other values (42)1081
42.5%
Common
ValueCountFrequency (%)
/274
29.0%
.133
14.1%
:67
 
7.1%
%57
 
6.0%
-46
 
4.9%
044
 
4.7%
938
 
4.0%
137
 
3.9%
835
 
3.7%
233
 
3.5%
Other values (12)182
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/274
 
7.8%
t266
 
7.6%
s182
 
5.2%
e159
 
4.6%
o158
 
4.5%
w143
 
4.1%
.133
 
3.8%
h127
 
3.6%
a112
 
3.2%
i109
 
3.1%
Other values (64)1829
52.4%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.725
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:16.528848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.9
Q114
median20.5
Q343.25
95-th percentile85
Maximum88
Range87
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation24.38456401
Coefficient of variation (CV)0.8203385706
Kurtosis0.2273424555
Mean29.725
Median Absolute Deviation (MAD)10.5
Skewness1.148847933
Sum2378
Variance594.606962
MonotonicityNot monotonic
2022-05-09T21:00:16.622824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
187
 
8.8%
175
 
6.2%
854
 
5.0%
144
 
5.0%
84
 
5.0%
273
 
3.8%
573
 
3.8%
63
 
3.8%
253
 
3.8%
242
 
2.5%
Other values (30)42
52.5%
ValueCountFrequency (%)
11
 
1.2%
21
 
1.2%
32
2.5%
52
2.5%
63
3.8%
72
2.5%
84
5.0%
102
2.5%
111
 
1.2%
144
5.0%
ValueCountFrequency (%)
881
 
1.2%
861
 
1.2%
854
5.0%
791
 
1.2%
772
2.5%
641
 
1.2%
601
 
1.2%
573
3.8%
552
2.5%
531
 
1.2%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
nan
79 
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}
 
1

Length

Max length66
Median length3
Mean length3.7875
Min length3

Characters and Unicode

Total characters303
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan79
98.8%
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}1
 
1.2%

Length

2022-05-09T21:00:16.725392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:00:16.819080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan79
92.9%
name1
 
1.2%
ukraine1
 
1.2%
code1
 
1.2%
ua1
 
1.2%
timezone1
 
1.2%
europe/zaporozhye1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n161
53.1%
a82
27.1%
'12
 
4.0%
e7
 
2.3%
o5
 
1.7%
5
 
1.7%
:3
 
1.0%
r3
 
1.0%
i2
 
0.7%
p2
 
0.7%
Other values (17)21
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter273
90.1%
Other Punctuation18
 
5.9%
Space Separator5
 
1.7%
Uppercase Letter5
 
1.7%
Open Punctuation1
 
0.3%
Close Punctuation1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n161
59.0%
a82
30.0%
e7
 
2.6%
o5
 
1.8%
r3
 
1.1%
i2
 
0.7%
p2
 
0.7%
z2
 
0.7%
m2
 
0.7%
u1
 
0.4%
Other values (6)6
 
2.2%
Other Punctuation
ValueCountFrequency (%)
'12
66.7%
:3
 
16.7%
,2
 
11.1%
/1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
U2
40.0%
Z1
20.0%
E1
20.0%
A1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
{1
100.0%
Close Punctuation
ValueCountFrequency (%)
}1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin278
91.7%
Common25
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n161
57.9%
a82
29.5%
e7
 
2.5%
o5
 
1.8%
r3
 
1.1%
i2
 
0.7%
p2
 
0.7%
z2
 
0.7%
U2
 
0.7%
m2
 
0.7%
Other values (10)10
 
3.6%
Common
ValueCountFrequency (%)
'12
48.0%
5
20.0%
:3
 
12.0%
,2
 
8.0%
/1
 
4.0%
{1
 
4.0%
}1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n161
53.1%
a82
27.1%
'12
 
4.0%
e7
 
2.3%
o5
 
1.7%
5
 
1.7%
:3
 
1.0%
r3
 
1.0%
i2
 
0.7%
p2
 
0.7%
Other values (17)21
 
6.9%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Memory size768.0 B
nan
16 
<p>Applying the laws of life on Earth to the rest of the galaxy, this series blends science fact and fiction to imagine alien life on other planets.</p>
 
4
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>
 
2
<p>The police are investigating a case that involves a death directly caused by a rare bug known as the bullet ant. In order to clear his name, Tan Jingtian, an Insect toxicology graduate becomes involved in the bizzare investigation and collaborates with forensic doctor Jin Ling. As they dig deeper, they uncover the mystery behind his own identity.</p><p>Along with police captain Chen Han and the other detectives, they trace every clue as they solve one case at a time to uncover the murderer that has been in hiding for many years.</p>
 
2
<p>Pan, a desolate plastic surgeon, lived a repetitive and boring life every day until a conspiracy happened. He woke up in an abandoned factory, and found that someone had replaced his identity with a face exactly like him. His world has been completely overturned and left in a perilous situation. Can he overcome the difficulties and peel away the truth? How would he regain his identity?</p>
 
2
Other values (48)
54 

Length

Max length1084
Median length442.5
Mean length255.5375
Min length3

Characters and Unicode

Total characters20443
Distinct characters84
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)52.5%

Sample

1st rownan
2nd rownan
3rd row<p><b>Mermaid Prince</b> is about Hye Ri, who embarks on a graduation trip to Gangwon Province and meets Woo Hyuk, a mysterious guy who runs a guest house.</p>
4th row<p>The pure and cute little devil Nini lives in Room 1031 of Wan Sheng Street Apartment. His roommates are no ordinary people: The lazy and wacky vampire Ira who spends most of his time gaming or watching TV, the unlucky dancer werewolf Vladimir, the serious and old-fashioned angel landlord Lynn and his naive little sister Lily, and many more.<br /><br />What none of them realize is that the power of the demon king is sleeping inside of Nini. With evil forces in hot pursuit, can Nini and his friends head off disaster?</p>
5th row<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>

Common Values

ValueCountFrequency (%)
nan16
 
20.0%
<p>Applying the laws of life on Earth to the rest of the galaxy, this series blends science fact and fiction to imagine alien life on other planets.</p>4
 
5.0%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.5%
<p>The police are investigating a case that involves a death directly caused by a rare bug known as the bullet ant. In order to clear his name, Tan Jingtian, an Insect toxicology graduate becomes involved in the bizzare investigation and collaborates with forensic doctor Jin Ling. As they dig deeper, they uncover the mystery behind his own identity.</p><p>Along with police captain Chen Han and the other detectives, they trace every clue as they solve one case at a time to uncover the murderer that has been in hiding for many years.</p>2
 
2.5%
<p>Pan, a desolate plastic surgeon, lived a repetitive and boring life every day until a conspiracy happened. He woke up in an abandoned factory, and found that someone had replaced his identity with a face exactly like him. His world has been completely overturned and left in a perilous situation. Can he overcome the difficulties and peel away the truth? How would he regain his identity?</p>2
 
2.5%
<p>A story that follows a detective in the major crimes division of Nan Xing City Police Department. Together with a woman who has super memory, he upholds the law one case at a time in solving murders, burglaries and bringing down a narcotics manufacturing facility. Jing Chu is a young and capable detective. Due to his repeated merits from cracking big cases, he is promoted to the position of major crimes division vice-captain at Nan Xing city and starts to work alongside his new team. Because of a murder case, he meets Yang Mian Mian, a young woman who possesses a photographic memory. He soon realizes that Mian Mian seems to have a deep connection to his father's mysterious death many years ago. Meanwhile, a famous blogger and a member of an idol girl group die</p>2
 
2.5%
<p>Merchant Jiang Shuo and his odd specialist companion Qin Yi Heng purchase frequented houses to exchange them. In any case, alarming things start to occur and each spooky house is by all accounts part of a major riddle. Jiang Shuo, Yi Heng, and police officer Yuan Mu Qing attempt to understand the riddle.</p>2
 
2.5%
<p>A story that follows people whose lives are entangled due to a complicated case. While investigating a drug cartel as an undercover cop, Yan Jin falls in love with the beautiful coffee shop owner Ji Xiao'ou.</p>2
 
2.5%
<p>‎Shen Moo, has no idea what adventures she will face. Beauty, is one of the best observers of Internet resources, but soon, in her head creeps the idea that in the universe there are mermaids. It's hard to believe, but for some reason, she can't get it out of her mind. Soon, she will have to take responsibility to prove their existence, otherwise, the dispute with the professor will turn out to be a real failure for the heroine. Together with the rescue team, they went to help the victims in The Xine Bay. Meet Ahn Xin - an athlete, will turn her reality. Unbelievable, but the guy is perceived as a mermaid. Of course, Mu Xin liked it madly, because she found a key witness and will be able to insist on her own. However, the mermaid man does his best to avoid revealing his real world.‎</p>2
 
2.5%
<p>‎At the end of the 20th century, due to the sudden decision of Xin Shensheng, gao Shan's business went bankrupt. Gao Shan wants to prove his father's innocence, but on his way suddenly falls in love with the daughter of Xin Shensheng, Tsin Waugh. Learning about the intentions of Gao Shan, Xin Shansheng makes him quit his job. ‎<br /><br />‎Gao Shan decides to go to Hong Kong to start from scratch, where he meets a benefactor and earns his first million in his life. Under the guidance of a mentor, he goes to Beijing and becomes a well-known investor. Soon Gao Shan meets Tsin Vo, who became a financial headhunter. Can love help them find their way to each other again? ‎<br /><br />‎Based on the novel by Xiao Moli "Little Storm 1.0"‎</p>2
 
2.5%
Other values (43)44
55.0%

Length

2022-05-09T21:00:16.928981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the171
 
4.9%
and116
 
3.4%
to113
 
3.3%
a104
 
3.0%
of83
 
2.4%
in63
 
1.8%
his47
 
1.4%
with41
 
1.2%
on31
 
0.9%
that28
 
0.8%
Other values (1240)2665
77.0%

Most occurring characters

ValueCountFrequency (%)
3381
16.5%
e1917
 
9.4%
a1296
 
6.3%
t1257
 
6.1%
n1232
 
6.0%
i1168
 
5.7%
o1142
 
5.6%
s959
 
4.7%
r887
 
4.3%
h754
 
3.7%
Other values (74)6450
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15257
74.6%
Space Separator3383
 
16.5%
Uppercase Letter722
 
3.5%
Other Punctuation561
 
2.7%
Math Symbol412
 
2.0%
Dash Punctuation41
 
0.2%
Decimal Number37
 
0.2%
Format16
 
0.1%
Close Punctuation7
 
< 0.1%
Open Punctuation7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1917
12.6%
a1296
 
8.5%
t1257
 
8.2%
n1232
 
8.1%
i1168
 
7.7%
o1142
 
7.5%
s959
 
6.3%
r887
 
5.8%
h754
 
4.9%
l641
 
4.2%
Other values (19)4004
26.2%
Uppercase Letter
ValueCountFrequency (%)
S71
 
9.8%
T52
 
7.2%
W47
 
6.5%
H45
 
6.2%
M43
 
6.0%
A42
 
5.8%
L33
 
4.6%
E32
 
4.4%
D31
 
4.3%
I29
 
4.0%
Other values (16)297
41.1%
Other Punctuation
ValueCountFrequency (%)
,187
33.3%
.167
29.8%
/113
20.1%
'45
 
8.0%
"16
 
2.9%
!13
 
2.3%
?9
 
1.6%
:5
 
0.9%
4
 
0.7%
;1
 
0.2%
Decimal Number
ValueCountFrequency (%)
011
29.7%
18
21.6%
26
16.2%
94
 
10.8%
83
 
8.1%
32
 
5.4%
72
 
5.4%
61
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
-33
80.5%
7
 
17.1%
1
 
2.4%
Space Separator
ValueCountFrequency (%)
3381
99.9%
 2
 
0.1%
Math Symbol
ValueCountFrequency (%)
>206
50.0%
<206
50.0%
Format
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
)7
100.0%
Open Punctuation
ValueCountFrequency (%)
(7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15979
78.2%
Common4464
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1917
12.0%
a1296
 
8.1%
t1257
 
7.9%
n1232
 
7.7%
i1168
 
7.3%
o1142
 
7.1%
s959
 
6.0%
r887
 
5.6%
h754
 
4.7%
l641
 
4.0%
Other values (45)4726
29.6%
Common
ValueCountFrequency (%)
3381
75.7%
>206
 
4.6%
<206
 
4.6%
,187
 
4.2%
.167
 
3.7%
/113
 
2.5%
'45
 
1.0%
-33
 
0.7%
16
 
0.4%
"16
 
0.4%
Other values (19)94
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20410
99.8%
Punctuation28
 
0.1%
None5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3381
16.6%
e1917
 
9.4%
a1296
 
6.3%
t1257
 
6.2%
n1232
 
6.0%
i1168
 
5.7%
o1142
 
5.6%
s959
 
4.7%
r887
 
4.3%
h754
 
3.7%
Other values (66)6417
31.4%
Punctuation
ValueCountFrequency (%)
16
57.1%
7
25.0%
4
 
14.3%
1
 
3.6%
None
ValueCountFrequency (%)
 2
40.0%
æ1
20.0%
å1
20.0%
é1
20.0%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1633494593
Minimum1606418164
Maximum1652080636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size768.0 B
2022-05-09T21:00:17.058983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1606418164
5-th percentile1607697965
Q11612821081
median1639631841
Q31648767102
95-th percentile1651838472
Maximum1652080636
Range45662472
Interquartile range (IQR)35946021.25

Descriptive statistics

Standard deviation17082536.87
Coefficient of variation (CV)0.01045766356
Kurtosis-1.506623263
Mean1633494593
Median Absolute Deviation (MAD)11235799
Skewness-0.4509135253
Sum1.306795674 × 1011
Variance2.918130661 × 1014
MonotonicityNot monotonic
2022-05-09T21:00:17.184903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16483288104
 
5.0%
16357351792
 
2.5%
16518384632
 
2.5%
16196334992
 
2.5%
16096488852
 
2.5%
16090607262
 
2.5%
16508264802
 
2.5%
16064181642
 
2.5%
16341681392
 
2.5%
16095351412
 
2.5%
Other values (56)58
72.5%
ValueCountFrequency (%)
16064181642
2.5%
16071040921
1.2%
16076979652
2.5%
16090607262
2.5%
16095351412
2.5%
16096068542
2.5%
16096488852
2.5%
16099508251
1.2%
16102051551
1.2%
16108125261
1.2%
ValueCountFrequency (%)
16520806361
1.2%
16519339621
1.2%
16519332091
1.2%
16518386471
1.2%
16518384632
2.5%
16517773161
1.2%
16516880411
1.2%
16516456841
1.2%
16512533561
1.2%
16509836761
1.2%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size768.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2015818
 
1
https://api.tvmaze.com/episodes/1998673
 
1
https://api.tvmaze.com/episodes/1997815
 
1
https://api.tvmaze.com/episodes/1997814
 
1
Other values (75)
75 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3120
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/20158181
 
1.2%
https://api.tvmaze.com/episodes/19986731
 
1.2%
https://api.tvmaze.com/episodes/19978151
 
1.2%
https://api.tvmaze.com/episodes/19978141
 
1.2%
https://api.tvmaze.com/episodes/20833311
 
1.2%
https://api.tvmaze.com/episodes/19503691
 
1.2%
https://api.tvmaze.com/episodes/20927291
 
1.2%
https://api.tvmaze.com/episodes/19967981
 
1.2%
https://api.tvmaze.com/episodes/20176641
 
1.2%
Other values (70)70
87.5%

Length

2022-05-09T21:00:17.290106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/20158181
 
1.2%
https://api.tvmaze.com/episodes/19640001
 
1.2%
https://api.tvmaze.com/episodes/19954051
 
1.2%
https://api.tvmaze.com/episodes/20077601
 
1.2%
https://api.tvmaze.com/episodes/19857891
 
1.2%
https://api.tvmaze.com/episodes/20396221
 
1.2%
https://api.tvmaze.com/episodes/20396231
 
1.2%
https://api.tvmaze.com/episodes/23244271
 
1.2%
https://api.tvmaze.com/episodes/19986091
 
1.2%
Other values (70)70
87.5%

Most occurring characters

ValueCountFrequency (%)
/320
 
10.3%
p240
 
7.7%
s240
 
7.7%
e240
 
7.7%
t240
 
7.7%
o160
 
5.1%
a160
 
5.1%
i160
 
5.1%
.160
 
5.1%
m160
 
5.1%
Other values (16)1040
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2000
64.1%
Other Punctuation560
 
17.9%
Decimal Number560
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p240
12.0%
s240
12.0%
e240
12.0%
t240
12.0%
o160
8.0%
a160
8.0%
i160
8.0%
m160
8.0%
h80
 
4.0%
d80
 
4.0%
Other values (3)240
12.0%
Decimal Number
ValueCountFrequency (%)
9100
17.9%
288
15.7%
176
13.6%
353
9.5%
849
8.8%
047
8.4%
640
 
7.1%
439
 
7.0%
736
 
6.4%
532
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/320
57.1%
.160
28.6%
:80
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2000
64.1%
Common1120
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/320
28.6%
.160
14.3%
9100
 
8.9%
288
 
7.9%
:80
 
7.1%
176
 
6.8%
353
 
4.7%
849
 
4.4%
047
 
4.2%
640
 
3.6%
Other values (3)107
 
9.6%
Latin
ValueCountFrequency (%)
p240
12.0%
s240
12.0%
e240
12.0%
t240
12.0%
o160
8.0%
a160
8.0%
i160
8.0%
m160
8.0%
h80
 
4.0%
d80
 
4.0%
Other values (3)240
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/320
 
10.3%
p240
 
7.7%
s240
 
7.7%
e240
 
7.7%
t240
 
7.7%
o160
 
5.1%
a160
 
5.1%
i160
 
5.1%
.160
 
5.1%
m160
 
5.1%
Other values (16)1040
33.3%

Interactions

2022-05-09T21:00:09.159271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:47.233123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:52.513778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:55.109980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:57.601626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:59.443464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:03.316686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:05.120268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:07.038147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:09.881518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:48.486924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:53.393502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:56.185122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:58.268507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:00.490223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:03.979566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:05.808624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:07.865859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:09.995415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:49.003169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:53.539055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:56.336310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:58.369402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:00.776634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:04.077657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:05.928381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:07.993403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:10.094301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:49.359128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:53.716251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:56.455764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:58.469539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:01.043873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:04.173487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:06.032991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:08.094534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:10.188641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:49.787594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:53.844732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:56.588404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:58.577306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:01.356869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:04.271461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:06.127981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:08.197345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:10.720795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:51.099891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:54.683845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:57.207693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:59.067184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:02.215071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:04.708845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:06.636703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:08.782224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:10.813796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:51.415498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:54.792989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:57.306319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:59.159281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:02.442358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:04.817225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:06.734460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:08.878443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:10.913314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:51.766845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:54.897756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:57.414346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:59.255868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:02.732325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:04.922403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:06.838826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:08.971857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:11.007682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:52.134850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:55.009593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:57.506506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:59.350459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:03.031977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:05.020680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:06.932973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:00:09.067408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:00:17.368820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:00:17.510797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:00:17.671939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:00:17.823191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:00:18.168254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:00:11.193212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:00:11.984842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:00:12.178972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:00:12.303242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01972782https://www.tvmaze.com/episodes/1972782/kontakty-1x28-kontakty-v-telefone-sergea-lazareva-timati-polina-gagarina-vlad-topalov-ida-galicКОНТАКТЫ в телефоне Сергея Лазарева: Тимати, Полина Гагарина, Влад Топалов, Ида Галич1.028.0regular2020-12-0212:002020-12-02T00:00:00+00:0038.0nan49630https://www.tvmaze.com/shows/49630/kontaktyКонтактыGame ShowRussian[]Running30.041.02019-04-03nanhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI53.0nannan1.651688e+09https://api.tvmaze.com/episodes/1977902
11979223https://www.tvmaze.com/episodes/1979223/kotiki-1x03-seria-3Серия 31.03.0regular2020-12-02nan2020-12-02T00:00:00+00:0013.0nan52198https://www.tvmaze.com/shows/52198/kotikiКотикиScriptedRussian['Comedy']Ended12.012.02020-11-302020-12-11http://epic-media.ru/project/kotiki15.0nannan1.637555e+09https://api.tvmaze.com/episodes/2015818
21971567https://www.tvmaze.com/episodes/1971567/mermaid-prince-2x07-episode-7Episode 72.07.0regular2020-12-0211:002020-12-02T02:00:00+00:0015.0nan47207https://www.tvmaze.com/shows/47207/mermaid-princeMermaid PrinceScriptedKorean['Drama', 'Romance', 'Mystery']Ended15.015.02020-04-142020-12-10nan34.0nan<p><b>Mermaid Prince</b> is about Hye Ri, who embarks on a graduation trip to Gangwon Province and meets Woo Hyuk, a mysterious guy who runs a guest house.</p>1.610205e+09https://api.tvmaze.com/episodes/1964000
31983072https://www.tvmaze.com/episodes/1983072/wan-sheng-jie-2x10-all-products-funds-were-spent-on-this-episodeAll products funds were spent on this episode2.010.0regular2020-12-0210:002020-12-02T02:00:00+00:004.0nan48395https://www.tvmaze.com/shows/48395/wan-sheng-jieWan Sheng JieAnimationChinese['Comedy', 'Anime', 'Supernatural']Running4.04.02020-04-01nanhttps://v.qq.com/detail/a/awnia0n2erqryf3.html17.0nan<p>The pure and cute little devil Nini lives in Room 1031 of Wan Sheng Street Apartment. His roommates are no ordinary people: The lazy and wacky vampire Ira who spends most of his time gaming or watching TV, the unlucky dancer werewolf Vladimir, the serious and old-fashioned angel landlord Lynn and his naive little sister Lily, and many more.<br /><br />What none of them realize is that the power of the demon king is sleeping inside of Nini. With evil forces in hot pursuit, can Nini and his friends head off disaster?</p>1.647194e+09https://api.tvmaze.com/episodes/1995405
41985615https://www.tvmaze.com/episodes/1985615/yi-nian-yong-heng-1x19-episode-19Episode 191.019.0regular2020-12-0210:002020-12-02T02:00:00+00:0019.0nan49652https://www.tvmaze.com/shows/49652/yi-nian-yong-hengYi Nian Yong HengAnimationChinese['Comedy', 'Action', 'Anime', 'Fantasy']Running19.019.02020-08-12nanhttps://v.qq.com/detail/w/ww18u675tfmhas6.html17.0nan<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>1.649494e+09https://api.tvmaze.com/episodes/2007760
52030018https://www.tvmaze.com/episodes/2030018/dolls-frontline-2x10-episode-10Episode 102.010.0regular2020-12-0212:002020-12-02T04:00:00+00:005.0nan45713https://www.tvmaze.com/shows/45713/dolls-frontlineDolls' FrontlineAnimationChinese['Comedy', 'Anime', 'Science-Fiction']Ended5.05.02019-07-282020-12-16https://www.bilibili.com/bangumi/media/md2822989520.0nan<p>Re-imagines famous firearms as moe girls with machine bodies that are known as T-Dolls.</p>1.613087e+09https://api.tvmaze.com/episodes/1985789
61973540https://www.tvmaze.com/episodes/1973540/please-wait-brother-1x19-episode-19Episode 191.019.0regular2020-12-0212:002020-12-02T04:00:00+00:0037.0nan52038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese['Comedy']Ended37.037.02020-11-172020-12-08nan14.0nannan1.607698e+09https://api.tvmaze.com/episodes/2039622
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Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
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751960029https://www.tvmaze.com/episodes/1960029/goede-tijden-slechte-tijden-31x55-aflevering-6310Aflevering 631031.055.0regular2020-12-0220:002020-12-02T19:00:00+00:0023.0nan2504https://www.tvmaze.com/shows/2504/goede-tijden-slechte-tijdenGoede Tijden, Slechte TijdenScriptedDutch['Drama', 'Romance']Running23.025.01990-10-01nanhttp://gtst.nl/#!/77.0nannan1.651839e+09https://api.tvmaze.com/episodes/1949334
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781976920https://www.tvmaze.com/episodes/1976920/ruth-ruby-virtual-sleepover-challenges-2020-12-02-holiday-jingle-wrap-challengeHoliday Jingle Wrap Challenge2020.08.0regular2020-12-0221:252020-12-03T02:25:00+00:005.0<p>Happy Holidays! BFFs Ruby Rose Turner (Coop &amp; Cami Ask the World), Ruth Righi (Sydney to the Max), and special guest Issac Ryan Brown (Raven's Home) compete to see who can wrap the most gifts using oven mits and then unwrap them without making any noise!</p>45434https://www.tvmaze.com/shows/45434/ruth-ruby-virtual-sleepover-challengesRuth & Ruby Virtual Sleepover ChallengesTalk ShowEnglish['Comedy', 'Children', 'DIY']Running12.06.02019-08-09nanhttps://disneynow.com/shows/ruth-and-ruby-virtual-sleepover-challenges21.0nan<p>Grab your sleeping bag and join BFFs Ruby Rose Turner (Coop &amp; Cami Ask the World) and Ruth Righi (Sydney to the Max) for the ultimate sleepover!</p>1.639238e+09https://api.tvmaze.com/episodes/1996786
791945144https://www.tvmaze.com/episodes/1945144/noblesse-1x09-blood-contract-devoteBlood Contract / Devote1.09.0regular2020-12-0200:002020-12-03T05:00:00+00:0025.0<p>Raizel and Frankenstein first met in Lukedonia, the country of nobles. Raizel took Frankenstein, who was a noble hunter at the time, into his mansion to work as his butler. Frankenstein protected him for several years, but then some among the nobility who had unfavorable opinions of the duo began to revolt. What decision did Frankenstein and Raizel reach in response to this conflict?</p>49732https://www.tvmaze.com/shows/49732/noblesseNoblesseAnimationJapanese['Anime', 'Supernatural']Ended25.025.02015-12-042020-12-30https://noblesse-anime.com/44.0nan<p>Raizel awakens from his 820-year slumber. He holds the special title of Noblesse, a pure-blooded Noble and protector of all other Nobles. In an attempt to protect Raizel, his servant Frankenstein enrolls him at Ye Ran High School, where Raizel learns the simple and quotidian routines of the human world through his classmates. However, the Union, a secret society plotting to take over the world, dispatches modified humans and gradually encroaches on Raizel's life, causing him to wield his mighty power to protect those around him... After 820 years of intrigue, the secrets behind his slumber are finally revealed, and Raizel's absolute protection as the Noblesse begins!</p>1.648717e+09https://api.tvmaze.com/episodes/1955318